class_docstr: An implementation for an explainable neural network for natural
  language processing and generating rationales used by that network.
  Based on the paper "Rethinking Cooperative Rationalization: Introspective
  Extraction and Complement Control" by Yu et. al.
code: def _init_rl_optimizers(self):
      self.opt_G_sup = torch.optim.Adam(
          filter(
              lambda x: x.requires_grad, self.generator.parameters()
          ),
          lr=self.lr,
      )
      self.opt_G_rl = torch.optim.Adam(
          filter(
              lambda x: x.requires_grad, self.generator.parameters()
          ),
          lr=self.lr * 0.1,
      )
  
imports: import logging
  import os
  import random
  from collections import deque
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from interpret_text.experimental.common.base_text_model import BaseTextModel
  from torch.autograd import Variable
  from tqdm import tqdm
  from interpret_text.experimental.common.utils_introspective_rationale import generate_data
label_desc: Use image node_id 3 for calling the IntrospectiveRationaleModel obj's underlying member method code with example usage: obj._init_rl_optimizers() without return types